Our aim is to acquire automated image analysis equipment and integrate it within the larger research instrumentation and personnel complex of the institution's Neuroscience Image Graphics Laboratory. The request centers on a Bausch and Lomb Omnicon 3000 Image Analysis System and a linked, dedicated Zeiss Universal Light Microscope. These units, plus an existing J.E.O.L. 100B electron microscope previously fitted with motorized staging, forms an automated morphometric feature extraction system to "front-end" the Laboratory's dedicated D.E.C. VAX 11/780 computer. This front-end system, paired with the extreme raw computational power of the VAX 11/780, makes many quantitative structural problems accessible for the first time. The resulting system is further enhanced by a large existing inventory of associated hardware, including a Megatek 3-D graphics system, Optronics digital drum densitometer, serial image registration devices, Vanguard motion analyzer and Numonics digitizer, optical graphics image mixing equipment and Hewlett-Packard plotter. The biomedical justification for this equipment lies in the complexity of the structural organization of the mammalian nervous system, whose development and diseases constitute the focus of our multidisciplinary investigation. This structural organization must be reconstructed from analysis of accurately-registered serial slices of tissue, each slice containing a very large number of data items (cells, nerve fibers, synapses, etc.), and each item to be categorized by size, shape, density, absolute position, and near-neighbor relationships. The primary assemblage of quantitative data, as well as its storage and recall in fluently accessible numerical and graphic form, requires automation. The funded N.I.H. grants to be supported by the proposed new instrumentation include studies on genetic control of brain development, determination of growth vectors of embryonic and regenerating axons, myelination, sexual differentiation of the brain, and sodium channel distribution in cell membranes. These projects share common machine-readable data elements, such as recognition of relatively simple and manageable contours and groupings based on density, size, and shape criteria, e.g., myelinated nerve fibers in transverse section, distinctively shaped and positioned cerebellar neuronal populations, or silver grains in autoradiograms.